The pipeline relation extraction approaches have an external dependency on an effective named entity recognition system. To remove that dependency, we explore joint entity and relation extraction in this work. A sentence may have multiple relation tuples and these tuples may share one or both entities among them. Extracting such relation tuples is a difficult task and sharing of entities or overlapping entities among the tuples makes it more challenging. Most prior work that adopted the pipeline approach cannot capture the interaction among the relation tuples in a sentence in an effective way. In this work, we propose two approaches to use encoder-decoder architecture for jointly extracting entities and relations, where relation tuples are generated in a sequence. In the first approach, we propose a representation scheme for relation tuples that enables the decoder to generate one word at a time, like machine translation models and still finds all the tuples present in a sentence with full entity names of different length and with overlapping entities. Next, we propose a pointer network-based decoding approach where an entire tuple is generated at every time step of the decoding process. Experiments on the publicly available New York Times corpus show that our proposed approaches outperform previous work and achieve significantly higher F1 scores. Material from this chapter has been published in [1]}.
